Optimal Micro-Data Switching: An Enhanced Framework and Decision Tool for Confid

最优微数据切换：Confid 的增强框架和决策工具

• 批准号: 7535414
• 负责人: James P. Kelly
• 金额: $ 9.98万
• 依托单位: OPTTEK SYSTEMS, INC.
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-10 至 2009-09-11
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7535414
• 关键词: Accounting; American; Area; Cells; Censuses; Centers for Disease Control and Prevention (U.S.); Client; Computational Biology; Confidentiality; Data; Data Analyses; Data Reporting; Data Set; Databases; Decision Analysis; Decision Making; Development; Disadvantaged; Disclosure; Economics; Elements; Ensure; Environment; Goals; Health Personnel; Healthcare; Incentives; Individual; Inferior; Institution; Machine Learning; Market Research; Medical Research; Methodology; Methods; National Security; Oils; Online Systems; Persons; Pharmaceutical Preparations; Policies; Policy Making; Privacy; Private Sector; Problem Solving; Process; Property; Provider; Public Health; Public Sector; Publishing; Purpose; Records; Research; Research Methodology; Respondent; Sales; Services; Social Welfare; Societies; Solutions; Structure; Support of Research; System; Techniques; Time; United States National Institutes of Health; base; computer framework; data mining; desire; innovation; interest; prevent; tool

DESCRIPTION (provided by applicant): The objective of this project is the development of an innovative technique to avoid disclosure of confidential data in public use tabular data. Our proposed technique, called Optimal Data Switching (OS), overcomes the limitations and disadvantages found in currently deployed disclosure limitation methods. Statistical databases for public use pose a critical problem of identifying how to make the data available for analysis without disclosing information that would infringe on privacy, violate confidentiality, or endanger national security. Organizations in both the public and private sectors have a major stake in this confidentiality protection problem, given the fact that access to data is essential for advancing research and formulating policy. Yet, the possibility of extracting certain sensitive elements of information from the data can jeopardize the welfare of these organizations and potentially, in some instances, the welfare of the society in which they operate. The challenge is, therefore, to represent the data in a form that permits accurate analysis for supporting research, decision-making and policy initiatives, while preventing an unscrupulous or ill-intentioned party from exploiting the data for harmful consequences. Our goal is to build on the latest advances in optimization, to which the OptTek Systems, Inc. (OptTek) research team has made pioneering contributions, to provide a framework based on optimal data switching, enabling the Centers for Disease Control and Prevention (CDC) and other organizations to effectively meet the challenge of confidentiality protection. The framework we propose is structured to be easy to use in a wide array of application settings and diverse user environments, from client-server to web-based, regardless of whether the micro-data is continuous, ordinal, binary, or any combination of these types. The successful development of such a framework, and the computer-based method for implementing it, is badly needed and will be of value to many types of organizations, not only in the public sector but also in the private sector, for whom the incentive to publish data is both economic as well as scientific. Examples in the public sector are evident, where organizations like CDC and the U.S. Census Bureau exist for the purpose of collecting, analyzing and publishing data for analysis by other parties. Numerous examples are also encountered in the private sector, notably in banking and financial services, healthcare (including drug companies and medical research institutions), market research, oil exploration, computational biology, renewable and sustainable energy, retail sales, product development, and a wide variety of other areas. PUBLIC HEALTH RELEVANCE: In the process of accumulating and disseminating public health data for reporting purposes, various uses, and statistical analysis, we must guarantee that individual records describing each person or establishment are protected. Organizations in both the public and private sectors have a major stake in this confidentiality protection problem, given the fact that access to data is essential for advancing research and formulating policy. This project proposes the development of a robust methodology and practical framework to deliver an efficient and effective tool to protect the confidentiality in published tabular data.

描述（由申请人提供）：该项目的目标是开发一种创新技术，以避免在公共使用表格数据时泄露机密数据。我们提出的技术，称为最佳数据交换（OS），克服了目前部署的披露限制方法的局限性和缺点。供公众使用的统计数据库提出了一个关键问题，即确定如何在不披露侵犯隐私、违反保密或危害国家安全的信息的情况下使数据可供分析。考虑到获取数据对于推进研究和制定政策至关重要，公共和私营部门的组织在这个保密问题上都有重大利益。然而，从数据中提取某些敏感信息的可能性可能危及这些组织的福利，在某些情况下可能危及它们所处社会的福利。因此，挑战在于，如何以一种允许对支持研究、决策和政策举措进行准确分析的形式表示数据，同时防止无良或恶意的一方利用数据产生有害后果。我们的目标是在OptTek Systems， Inc. （OptTek）研究团队做出开创性贡献的最新优化进展的基础上，提供基于最佳数据交换的框架，使疾病控制和预防中心（CDC）和其他组织能够有效地应对机密保护的挑战。无论微数据是连续的、有序的、二进制的，还是这些类型的任何组合，我们提出的框架的结构都易于在广泛的应用程序设置和不同的用户环境中使用，从客户端-服务器到基于web。这种框架的成功发展和以计算机为基础的执行方法是迫切需要的，而且不仅对公共部门而且对私营部门的许多类型的组织都是有价值的，对这些组织来说，公布数据的动机既是经济上的，也是科学上的。公共部门的例子很明显，像疾病预防控制中心和美国人口普查局这样的组织存在的目的是收集、分析和发布数据，供其他各方分析。在私营部门，特别是在银行和金融服务、医疗保健（包括制药公司和医学研究机构）、市场研究、石油勘探、计算生物学、可再生和可持续能源、零售销售、产品开发和各种其他领域，也遇到了许多这样的例子。公共卫生相关性：在为报告目的、各种用途和统计分析而积累和传播公共卫生数据的过程中，我们必须保证描述每个人或每个机构的个人记录受到保护。考虑到获取数据对于推进研究和制定政策至关重要，公共和私营部门的组织在这个保密问题上都有重大利益。该项目建议开发一种强有力的方法和实用框架，以提供一种高效和有效的工具来保护已发布的表格数据的机密性。